Proceedings of the 11th Annual conference on Genetic and evolutionary computation最新文献_第9页

Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap 演化算法在具有最近邻相互作用和可调重叠的自然景观上的性能

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570018

M. Pelikán, K. Sastry, D. Goldberg, Martin Volker Butz, Mark Hauschild

引用次数: 43

Approximating geometric crossover in semantic space 语义空间中的近似几何交叉

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570036

K. Krawiec, Pawel Lichocki

引用次数: 104

Session details: Track 13: real world application 会议细节:轨道13:真实世界的应用

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/3257507

M. O’Neill

引用次数: 0

Visualizing the search process of particle swarm optimization 粒子群优化的搜索过程可视化

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569909

Yong-Hyuk Kim, K. Lee, Yourim Yoon

引用次数: 14

Multi-objective optimization with an evolutionary artificial neural network for financial forecasting 基于进化人工神经网络的财务预测多目标优化

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570096

Matthew Butler, Ali Daniyal

引用次数: 14

A genetic algorithm for analyzing choice behavior with mixed decision strategies 混合决策策略下选择行为分析的遗传算法

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570113

Jella Pfeiffer, D. Duzevik, Franz Rothlauf, Koichi Yamamoto

{"title":"A genetic algorithm for analyzing choice behavior with mixed decision strategies","authors":"Jella Pfeiffer, D. Duzevik, Franz Rothlauf, Koichi Yamamoto","doi":"10.1145/1569901.1570113","DOIUrl":"https://doi.org/10.1145/1569901.1570113","url":null,"abstract":"In the field of decision-making a fundamental problem is how to uncover people's choice behavior. While choices them- selves are often observable, our underlying decision strategies determining these choices are not entirely understood. Previous research defined a number of decision strategies and conjectured that people do not apply only one strategy but switch strategies during the decision process. To the best of our knowledge, empirical evidence for the latter conjecture is missing. This is why we monitored the purchase decisions 624 consumers shopping online. We study how many of the observed choices can be explained by the existing strategies in their pure form, how many decisions can be explained if we account for switching behavior, and investigate switching behavior in detail. Since accounting for switching leads to a large search space of possible mixed decision strategies, we apply a genetic algorithm to find the set of mixed decision strategies which best explains the observed behavior. The results show that mixed strategies are used more often than pure ones and that a set of four mixed strategies is able to explain 93.9% of choices in a scenario with 4 alternatives and 75.4% of choices in a scenario with 7 alternatives.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130486444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A cooperative and self-adaptive metaheuristic for the facility location problem 设施选址问题的合作自适应元启发式算法

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569946

D. Meignan, Jean-Charles Créput, A. Koukam

引用次数: 5

An extended evolution strategy for the characterization of fracture conductivities from well tests 从试井中描述裂缝导流性的扩展演化策略

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570095

J. Bruyelle, A. Lange

{"title":"An extended evolution strategy for the characterization of fracture conductivities from well tests","authors":"J. Bruyelle, A. Lange","doi":"10.1145/1569901.1570095","DOIUrl":"https://doi.org/10.1145/1569901.1570095","url":null,"abstract":"The characterization of fractured reservoirs involves: (1) the design of geological models integrating statistical and/or deterministic fracture properties; (2) the validation of flow simulation models by calibrating with dynamic field data e.g. well tests. The latter validation step is critical since it also validates the underlying geological model, it allows one to reduce some uncertainties among the fracture geometrical and distribution properties, and it is often the only mean to characterize fracture conductivities. However this is usually an ill-posed inverse problem: field data are usually not sufficient to fully characterize the fracture system. It is of interest to explore the parameters space effectively, so that multiple solutions may be characterized, and many production development scenarii may be studied. This paper presents a well tests inversion method to characterize fracture sets conductivities. The Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) has been used as the optimization algorithm. It has been tested with some local optimization algorithms for comparison, and extended in order to detect several solutions simultaneously using a local proxy of the response surface. Moreover, uncertainty analyses are performed in regions of interest. Applications are presented for a fracture system with two fracture sets.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132994158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Uncertainty handling CMA-ES for reinforcement learning 用于强化学习的不确定性处理CMA-ES

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570064

V. Heidrich-Meisner, C. Igel

引用次数: 14

Multiobjectivization for parameter estimation: a case-study on the segment polarity network of drosophila 参数估计的多目标化:以果蝇片段极性网络为例

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569931

T. Hohm, E. Zitzler

{"title":"Multiobjectivization for parameter estimation: a case-study on the segment polarity network of drosophila","authors":"T. Hohm, E. Zitzler","doi":"10.1145/1569901.1569931","DOIUrl":"https://doi.org/10.1145/1569901.1569931","url":null,"abstract":"Mathematical modeling for gene regulative networks (GRNs) provides an effective tool for hypothesis testing in biology. A necessary step in setting up such models is the estimation of model parameters, i.e., an optimization process during which the difference between model output and given experimental data is minimized. This parameter estimation step is often difficult, especially for larger systems due to often incomplete quantitative data, the large size of the parameter space, and non-linearities in system behavior. Addressing the task of parameter estimation, we investigate the influence multiobjectivization can have on the optimization process. On the example of an established model for the segment polarity GRN in Drosophila, we test different multiobjectivization scenarios compared to a singleobjective function proposed earlier for the parameter optimization of the segment polarity network model. Since, instead of a single optimal parameter setting, a set of optimal parameter settings exists for this GRN, the comparison of the different optimization scenarios focuses on the capabilities of the different scenarios to identify optimal parameter settings showing good diversity in the parameter space. By embedding the objective functions in an evolutionary algorithm (EA), we show the superiority of the multiobjective approaches in exploring the model's parameter space.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133067286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7